types of machine learning models

category. Emotional analysis After about a week on the fake news site, I began to wonder if I had overly complicated the problem. Maybe I just need to use a ready-made machine learning model for emotional analysis to see if there is a pattern. I decided to build a quick gadget, using a web crawler to grab the title, description, author, and content of the article, and to enter the crawl results into the af

clustering. Select the appropriate machine learning task for a potential application. Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. Represent your data as features to serve as input to machine learning

Because Linux is hot now, more and more people are learning Linux, but it is very wasteful to buy a service to study at home. So how to solve this problem? Virtual machine software is a good choice, commonly used virtual machine software has VMware workstations and virtual box and so on. When using virtual machine soft

) Convolutional Neural Network Cascade automatic encoder (SAE) Dimensionality Reduction Method Like the clustering method, the Dimensionality Reduction Method tries to use the internal structure of the data to summarize or describe the data. The difference is that it uses less information in an unsupervised manner. This is helpful for visualizing high-dimensional data or simplifying data for subsequent supervised learning. Principal Component Anal

I. Working methods of machine learning ① Select data: Divide your data into three groups: training data, validating data, and testing data ② model data: Using training data to build models using related features ③ validation Model: Using your validation data to access your model ④ Test Model: Use your test data to check the performance of the validated model

algorithms include q-learning and time difference learning (temporal difference learning)In the case of enterprise Data application, the most commonly used is the model of supervised learning and unsupervised learning. In the field of image recognition, semi-supervised

minimum functionRegular equation method gradient descent can be better extended to large datasets for a large number of contexts and machine learning next-important extensions The regular equation of extended numerical solution of two algorithms in order to solve the minimization problem of [min J (θ0,θ1)], we use the exact numerical method rather than the constant iterative gradient descent method with th

input data directly feedback to the model, the model must be immediately adjusted. Common application scenarios include dynamic systems and robot control. Common algorithms include q-learning and time difference learning (temporal difference learning)In the case of enterprise Data application, the most commonly used is the model of supervised

similarityAccording to the function and form similarity of the algorithm, we can classify the algorithm, for example, tree-based algorithm, neural network based algorithm and so on. Of course, the scope of machine learning is very large, and some algorithms are difficult to classify into a certain category. For some classifications, the same classification algorithm can be used for different